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Journal of Applied Science, Engineering, Technology, and Education
Published by PT. Mattawang Mediatama Solution
ISSN : -     EISSN : 26850591     DOI : https://doi.org/10.35877/454RI.asci1116
Core Subject : Science, Education, Engineering,
Civil Engineering, Building, Construction & Architecture Computer Science & IT Electrical & Electronics Engineering Industrial & Manufacturing Engineering Other
Journal of Applied Science, Engineering, Technology, and Education (ASCI) is an international wide scope, peer-reviewed open access journal for the publication of original papers concerned with diverse aspects of science application, technology and engineering.
Arjuna Subject : Umum - Umum
Articles 15 Documents
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Search results for , issue "Vol. 7 No. 1 (2025)" : 15 Documents clear
Implementation of Machine Learning Algorithm with Extreme Gradient Boosting (XGBoost) Method In Hypertension Level Classification Rais, Zulkifli; Fahmuddin S, Muhammad; Saida, Saida; Triutomo, Agung
Journal of Applied Science, Engineering, Technology, and Education Vol. 7 No. 1 (2025)
Publisher : PT Mattawang Mediatama Solution

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35877/454RI.asci4191

Abstract

The increasing number of hypertension patients and the threat of serious complications make hypertension one of the leading causes of death worldwide. Early prevention is currently considered one of the best solutions. Early prevention through early detection can be achieved by utilizing machine learning technology. XGBoost is a machine learning algorithm based on gradient boosting machines. XGBoost applies regularization techniques to reduce overfitting and has faster execution speed as well as better performance. The objective of this research is to classify hypertension levels using the XGBoost method and leveraging hyperparameter tuning for optimization. In this study, the hyperparameter optimization technique used is gridsearchCV. The evaluation results of the XGBoost classification method using the best combination of parameters show good performance, where the XGBoost model achieves an accuracy of 93.3%, Precision of 97%, Recall of 92%, F1-Score of 93%, and AUC value of 0.935. This implies that the classification of hypertension levels in patients at Pelamonia Makassar Hospital can be well or accurately classified using the XGBoost method.
ARDL-Based Investigation of the Relationship between Monetary Policy and Inflation Mitigation Fatur, Hassan Ali Osman; Dawwas, Abdalla bin Mubarak; Abaker, Abdelgalal Osman Idris; Mohamed, Sami Elsir Ahmed
Journal of Applied Science, Engineering, Technology, and Education Vol. 7 No. 1 (2025)
Publisher : PT Mattawang Mediatama Solution

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35877/454RI.asci4206

Abstract

This study aims to estimate the efficiency of monetary policy in reducing inflation rates in Sudan for the years 2000 - 2022. The data of the study were collected from the Central Bank of Sudan and the Central Bureau of Statistics. To estimate the relationship between the variables, the study used statistical methods and econometric tools, including co integration error correction, and the Augmented Dickey-Fuller test. According to the results, there is a cointegration connection between the variables, and the variables are first order integrated. It was concluded that the estimated model is significant, and therefore it can be used to forecast, and that inflation is inversely related to both the exchange rate and the cost of financing, while the inflation is directly related to both bank credit and the money supply. To control inflation and stabilize exchange rates, a contractionary monetary policy was recommended
Pre-Trained Transformer-Based Approach for Arabic Question Answering: A Comparative Study Jamal, Amani; Alsubhi, Kholoud
Journal of Applied Science, Engineering, Technology, and Education Vol. 7 No. 1 (2025)
Publisher : PT Mattawang Mediatama Solution

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35877/454RI.asci4209

Abstract

Question answering (QA) is one of the most challenging yet widely investigated problems in Natural Language Processing (NLP). Question-answering (QA) systems try to produce answers for given questions. These answers can be generated from unstructured or structured text. Hence, QA is considered an important research area that can be used in evaluating text understanding systems. A large volume of QA studies was devoted to the English language, investigating the most advanced techniques and achieving state-of-the-art results. However, research efforts in the Arabic questionanswering progress at a considerably slower pace due to the scarcity of research efforts in Arabic QA and the lack of large benchmark datasets. Recently many pre-trained language models provided high performance in many Arabic NLP problems. In this work, we evaluate the state-of-the-art pre-trained transformers models for Arabic QA using four reading comprehension datasets which are Arabic-SQuAD), ARCD, AQAD, and TyDiQA-GoldP datasets. We fine-tuned and compared the performance of the AraBERTv2-base model, AraBERTv0.2-large model, and AraELECTRA model. In the last, we provide an analysis to understand and interpret the low-performance results obtained by some models.
Exploring Nano Simply Open Sets in Nano Topology: Applications and Insights El Sayed, M.
Journal of Applied Science, Engineering, Technology, and Education Vol. 7 No. 1 (2025)
Publisher : PT Mattawang Mediatama Solution

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35877/454RI.asci4215

Abstract

In recent times, nano set theory has shown broad applications in addressing practical challenges in various domains, including engineering, social sciences, and healthcare sciences. The notion of nano topological spaces, along with associated concepts like nano near open sets, has greatly developed because of its practical usefulness. The idea of nano continuity extends the idea of continuity. Likewise, nano open sets extend the notion of open sets within topological spaces. This paper presents a new class of nano near open sets, termed "nano simply open sets" and "nano delta sets," which expands and alters the current concepts of nano open sets (and, in certain instances, nano near open sets). We introduce a novel category of nano simply open sets, explore their characteristics, and analyze their connections with other sets. Additionally, we investigate new ideas like "nano simply continuous functions” and also, we introduce new notions of continuity based on the new notions. We shall study some of their properties and we will study the relationship between the new concepts and various other nano open sets.
Analysis of the Factors Affecting the Financial Performance of Insurance Companies using Statistical Modeling Younis A., Halima; Salem, Hamdy Mohamed; Alsanea, Mahmoud Selim; Elemam, Halla Z. S.; Abaker, Abdelgalal O. I.
Journal of Applied Science, Engineering, Technology, and Education Vol. 7 No. 1 (2025)
Publisher : PT Mattawang Mediatama Solution

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35877/454RI.asci4217

Abstract

The insurance industry is fundamental to the global economy, accounting for about 7% of the gross domestic product (GDP) in numerous developed nations and serving a crucial function in risk management and financial stability. Recent years have seen escalating economic pressures that have adversely affected the profitability of insurance firms. These Difficulties encompass escalating inflation rates, a surge in claims, and losses attributable to natural disasters, with swings in interest rates that have impacted investment returns and the valuation of financial portfolios. This study aims to examine the determinants influencing the financial performance of insurance businesses through precise statistical models, with a particular emphasis on return on equity (ROE) as a principal metric. The research utilized real-time data encompassing characteristics such as insurance density, interest rates, underwriting capacity, and insurance expenditures, among others. Statistical modeling was employed to ensure the degree to which these factors influence profitability. The project seeks to establish an analytical framework to improve the efficiency of underwriting and pricing decisions. It further advances academic literature by utilizing sophisticated analytical tools to understand profitability dynamics inside the insurance sector.

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